from cmdstanpy import CmdStanModel
model = CmdStanModel(stan_file='Lec25/bernoulli.stan')
fit = model.sample(data='Lec25/bernoulli.json')
Lecture 25
At the moment both Python & R offer two variants of Stan:
pystan & RStan - native language interface to the underlying Stan C++ libraries
CmdStanPy & CmdStanR - are wrappers around the CmdStan command line interface
model.stan)Any of the above tools will require a modern C++ toolchain (requires C++17 support).
Stan code is divided up into specific blocks depending on usage - all of the following blocks are optional but the ordering has to match what is given below.
functions {
// user-defined functions
}
data {
// declares the required data for the model
}
transformed data {
// allows the definition of constants and transforms of the data
}
parameters {
// declares the model’s parameters
}
transformed parameters {
// allows variables to be defined in terms of data and parameters
}
model {
// defines the log probability function
}
generated quantities {
// allows derived quantities based on parameters, data, and random number generation
}CmdStanMCMC: model=bernoulli chains=4['method=sample', 'algorithm=hmc', 'adapt', 'engaged=1']
csv_files:
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_1.csv
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_2.csv
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_3.csv
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_4.csv
output_files:
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_0-stdout.txt
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_1-stdout.txt
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_2-stdout.txt
/var/folders/v7/wrxd7cdj6l5gzr0191__m9lr0000gr/T/tmpcy2xjnn5/bernoulli2nghp52g/bernoulli-20250414213140_3-stdout.txt
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Rank-normalized split effective sample size satisfactory for all parameters.
Rank-normalized split R-hat values satisfactory for all parameters.
Processing complete, no problems detected.
Sta 663 - Spring 2025